Fault-Aware Matched Filter and Optical Flow

ABSTRACT

In one embodiment, a fault-aware matched filter augments the output of a component matched filter to provide both fault-aware matched filter output and a measure of confidence in the accuracy of the fault-aware matched filter output. In another embodiment, an optical flow engine derives, from a plurality of images, both optical flow output and a measure of confidence in the optical flow output. The measure of confidence may be derived using a fault-aware matched filter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following co-pending andcommonly owned U.S. provisional patent applications, both of which areincorporated by reference herein:

U.S. Prov. Pat. App. Ser. No. 61/181,664, filed on May 27, 2009,entitled, “Fault-Aware Matched Filter and Optical Flow”; and

U.S. Prov. Pat. App. Ser. No. 61/182,098, filed on May 28, 2009,entitled, “Fault-Aware Matched Filter and Optical Flow.”

BACKGROUND

A traditional matched filter correlates a known signal or function (the“template” or “reference”) with an unknown signal in the presence ofnoise to detect and select (match) the presence of the unknown signalfrom the reference set. One common application of matched filters is inradar systems, in which a known signal is transmitted, and in which thereflected signal received by the radar system is matched against theoriginally-transmitted known signal.

The concept of matched filters was first introduced by North in 1943 toincrease the signal-to-noise (SNR) ratio in early telecommunicationsapplications, although the modern name wasn't used until a 1960 paper byTurin. The current state-of-the-art, Principe's correntropy matchedfilter, is a nonlinear extension to the classical matched filter(International Patent WO 2007/027839 A2).

Traditional matched filters typically produce a numeric output thatdescribes the similarity between the reference and the unknown signal.Often, a threshold function is applied to this numeric output such thatthe final result represents a “match/no-match” result for thecomparison.

One common application of the matched filter in the area of machinevision is for the computation of optical flow. Optical flow (OF) is ameasurement of the apparent velocities of brightness patterns betweenimages in a sequence (Horn and Schunck 1981). The most common type ofinput image sequence in embedded “machine vision” applications is asequence of time-ordered frames from live or recorded video. Opticalflow in an image sequence results from changes in camera pose or changesin the environment, such as the motion of an object in the scene, ordynamically varying lighting conditions. The traditional output of theOF algorithm is a vector field showing the magnitude and direction ofthe optical flow of the input image sequence. In general, the vectorfield consists of one or more vectors representing the flow measured atlocations within the input data coordinate space. Individual flowvectors can have one or more dimensions, depending on the input data.For optical flow measurements of an image sequence sourced from livevideo, the origin of the vector indicates the position of the OF sample(i.e. measurement) in the source image space, and themagnitude/direction of the vector provides the magnitude anddisplacement of the optical flow field at the sample position.

Many classification systems for classes of optical flow algorithm exist.One widely accepted approach divides OF algorithms into five classes(Beauchemin and Barron 1995): intensity-based differential methods,correlation-based methods, frequency-based differential methods,multiple motion methods, and temporal refinement methods. Alternately,OF algorithms can be classified as “classical” or “real-time” todifferentiate according to the speed at which the data is processed. Inthe latter classification scheme, “real-time” implies that processingoccurs as the images are input to the system, and that the processingoutput is made available after some short processing delay. Furthermore,the application will provide a constraint on the maximum allowed delaybetween data input and optical flow output. By contrast, a “classical”optical flow algorithm has no time constraint for the processing. Shorttimes are always better, but long times are acceptable if justified byperformance. This alternate classification scheme is most frequentlyused when dealing with real-time embedded applications; for these apps,only real-time OF algorithms are of any use. Although “real-time”sometimes is defined as being ˜30 Hz (due to influence from traditionalvideo frame rates), there is no single agreed-upon quantitativedefinition for the term “real-time.”

The most famous optical flow algorithm is the KLT feature tracker, whichwas originally described in the work of Lucas and Kanade (Lucas andKanade 1981) and fully developed by Tomasi and Kanade (Tomasi and Kanade1991). Historically, a key problem in the computation of flow is theidentification of reliable features to track. This problem is addressedin a classical work by Shi and Tomasi (Shi and Tomasi 1994), wheremetrics were developed to identify “good features to track”, which wereregions of the image that were likely to be suitable for making areliable optical flow measurement. Additionally, Shi and Tomasi 1994presented an affine motion model for the motion of the features withinan image, which provided improved performance compared to the simplertranslational model. The KLT feature tracker is well understood byoptical flow experts. It is traditionally classified as a classical OFalgorithm, but it can be implemented in real-time for some applicationsusing modern embedded hardware, with reasonably good performance. Sinceit is well understood and since optical flow algorithms also containmatched filters, KLT will be used herein as an OF algorithm baseline forcomparison to embodiments of the inventive methods.

A mission- or safety-critical system is one where correct and safeoperation of a signal processing subsystem strongly influences theperformance/behavior of the system as a whole. For safety-criticalsubsystems, the performance of the system is directly related to humansafety. Modern real-time mission- and safety-critical systems mustimpose application-specific requirements on underlying signal processingsub-components in order to ensure that the algorithms meet the needs ofthe system. Unfortunately, challenging or harsh signal processingenvironments are common in extreme applications, and these can lead toalgorithm behavior that violates the design specification. Thisout-of-spec behavior is called a “signal processing fault” when itoccurs in signal processing algorithms like the matched filter oroptical flow.

Signal processing faults are a significant problem for small form-factormission- or safety-critical embedded systems. Faults that occur inlow-level signal processing layers in this type of system have increasedlikelihood to induce faults in higher-level signal processing,eventually leading to the failure of the system as a whole. In asafety-critical system, such failures can lead to property damage,injury, and even death. Unfortunately, the size, weight, and powerconsumption restrictions typically imposed on small form-factor embeddedelectronics impose severe limits on the fault detection methods that canbe used.

SUMMARY

Currently, state-of-the-art matched filters are capable of generatingfault warning outputs along with their matched filter output. Thesefault warnings assess the correctness or accuracy of the matched filteroutput such that a consumer of that output can elect to ignore theoutput or take fault-correction steps when a fault state is indicated.However, as will be shown in the Detailed Description section, the faultwarnings produced by conventional matched filters are derived from thesame filter data set that was used to create the matched filter output.As a result, the matched filter output and the fault warning outputshare substantially similar failure modes, such that an external eventcapable of inducing a fault condition in the matched filter output has asignificantly increased probability of also inducing a fault conditionin the fault warning output. In other words, an event capable ofbreaking the filter has an increased chance of breaking all of theoutputs, including the fault warnings.

This is a serious problem for a filter that is part of a mission- orsafety-critical system. In these systems, undetected faults can causeproperty damage and/or death if they propagate through the systemunchecked. As a result, such systems require expensive processingwithout access to the inner workings of the filter to independentlydetect scenarios of increased risk so mitigation steps can be taken.However, systems operating at the cutting edge of technology, inchallenging environments, or those with a wide range of operatingconditions often do not have the excess processing capacity to supportthe overhead of “detecting faults in the fault-detection”.

Embodiments of the present invention include a fault-aware matchedfilter which augments an underlying matched filter to derive and output“confidence estimate outputs”. The confidence estimate outputs of thefault aware-matched filter are derived in a manner such that they areguaranteed to have substantially different failure modes than theunderlying matched filter output. In this way, fault-aware matchedfilters implemented according to embodiments of the present inventionpossess a significantly higher probability of correctly detecting afault in the matched filter output compared to the un-augmented “legacy”(underlying) filter. The quantitative definition of “significantlyhigher probability” is dependant on the application in question (and theapplication's tolerance for risk). However, a good guideline is that thephrase implies an order of magnitude or more of improvement in the rateof undetected output matched filter output faults.

For example, one embodiment of the present invention is a method (suchas a computer-implemented method) comprising: (A) applying a firstfilter to a data vector and a set R, the set R containing at least onereference vector, to produce first filter output representing a measureof similarity of the data vector to at least one reference vector in theset R; (B) applying a second filter to second filter inputs to producesecond filter output, wherein the first filter has at least one failuremode not shared by the second filter; (C) deriving, from the firstfilter output and the second filter output, confidence estimate outputrepresenting a degree of confidence in the correctness of the firstfilter output. Other embodiments of the present invention includedevices and systems having components adapted to perform this method.

Optical flow algorithms are consumers of matched filter output such thateach optical flow sample is produced by a matched filter. Because themeasurement of optical flow is inherently error prone, these algorithmshave adapted to consume both the matched filter and fault warningoutputs for each flow sample. Any flow sample associated with assertedfault warning outputs from the originating matched filter is discardedby the optical flow algorithm. Undetected matched filter output faultsmanifest in the optical flow field as “outliers” or “flow samplefaults”. These flow sample faults are essentially “wrong-way” vectorswhich incorrectly report the optical flow at a region within the image.If enough undetected flow sample faults collect in the optical flowoutput then the optical flow algorithm's output can go out of itsoperating specification. When the optical flow output isout-of-specification, an “optical flow fault” or “flow-field fault” issaid to have occurred. Optical flow faults due to undetected flow samplefaults are very difficult to detect reliably after-the-fact with anyform of post-processing in “real-world” environments. When they occur,the optical flow algorithm's output consumer is at increased risk offailure. In the worst case scenario, the fault would continue topropagate to subsequent output consumers and cause the entire system tofail.

An important observation is that flow sample faults (detected orundetected) are not the only ways to cause an optical flow fault.Optical flow faults can occur any time the flow field output isout-of-spec, and the output specification can have(application-specific) requirements other than those related toindividual flow samples. For example, an optical flow algorithm might berequired to output more than some critical number of samples.Alternately, the flow-field density in a specified region-of-interestmight be part of the output specification. In the presence ofrequirements such as these, it is possible that all flow samples arevalid, yet the flow field itself is invalid. When application-specificrequirements are considered, it is clear that a need exists for faultdetection in optical flow beyond that provided by simple filtering ofthe flow field by individual matched filter fault warnings, as is doneby the current optical flow state-of-the-art.

Embodiments of the present invention for fault-aware optical flowinclude embodiments which generate optical flow confidence estimateoutputs that are similar to the confidence estimate outputs of matchedfilters implemented according to embodiments of the present invention,in that the fault-aware optical flow confidence estimate outputs assessthe “correctness” of the optical flow output relative to anapplication-specific output specification. One such embodiment utilizesone-or-more fault-aware matched filters to generate the optical flowvector field. Another such embodiment utilizes one-or-more unspecifiedfault-aware matched filters to generate the flow field and augments thetraditional optical flow output set with an optical flow confidenceestimate output. Yet another embodiment utilizes one-or-more unspecifiedmatched filters to generate the flow field and augments the traditionaloptical flow output set with an optical flow confidence estimate output.In this embodiment, no fault-aware matched filters are present. Yetanother embodiment utilizes supplemental sensor inputs as an estimationtool and as a fault-detection mechanism.

Embodiments of the present invention also include novel techniques forgenerating optical flow, referred to herein as correntropy optical flowand fault-aware correntropy optical flow (where the latter is anembodiment of the inventive methods for correntropy optical flow,augmented by an embodiment of inventive methods for fault-aware opticalflow).

For example, one embodiment of the present invention is a method (suchas a computer-implemented method) comprising: (A) receiving a firstimage input in an image input sequence; (B) receiving a second imageinput in the image input sequence, wherein the second image input has arelative position subsequent to the first image input in the image inputsequence; (C) applying a frame sample filter to the first image input toproduce a sample pattern comprising at least one sample; (D) applying asample registration filter to the second image input and the samplepattern to identify: (D)(1) an unfiltered flow sample set comprising atleast one optical flow sample, wherein the unfiltered flow sample setdefines a coregistration between the first image input and the secondimage input; (D)(2) at least one fault warning representing a degree ofconfidence in the coregistration; (E) removing, from the unfiltered flowsample set, at least one sample specified by the at least one faultwarning as in-fault, to produce a filtered flow sample set; and (F)generating optical flow confidence estimate output based the filteredflow sample set. Other embodiments of the present invention includedevices and systems having components adapted to perform this method.

Yet another embodiment of the present invention is a method (such as acomputer-implemented method) comprising: (A) receiving a first imageinput in an image input sequence; (B) receiving a second image input inthe image input sequence, wherein the second image input has a relativeposition subsequent to the first image input in the image inputsequence; (C) applying a frame sample filter, including a first matchedfilter, to the first image input to produce a sample pattern comprisingat least one sample; (D) applying a sample registration filter,including a second matched filter, to the second image input and thesample pattern to identify: (D)(1) an unfiltered flow sample setcomprising at least one optical flow sample, wherein the unfiltered flowsample set defines a coregistration between the first image input andthe second image input; and (D)(2) at least one fault warningrepresenting a degree of confidence in the coregistration; and (E)removing, from the unfiltered flow sample set, at least one samplespecified by the at least one fault warning as in-fault, to produce afiltered flow sample set. Other embodiments of the present inventioninclude devices and systems having components adapted to perform thismethod.

Other features and advantages of various aspects and embodiments of thepresent invention will become apparent from the following descriptionand from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a data flow diagram for an embodiment of a generic algorithmwith built-in fault detection. The diagram is intended for the purposeof explaining the nature of fault-detection capability, and is not arepresentation of any specific algorithm or algorithm class.

FIG. 2A is a data flow diagram of one embodiment of a traditional(legacy) matched filter with no fault detection and a reference vectorset containing one element.

FIG. 2B is a data flow diagram of one embodiment of a traditional(legacy) matched filter with fault detection and a reference vector setcontaining one element.

FIG. 3A is a data flow diagram of one embodiment of a traditional(legacy) matched filter with no fault detection and a reference vectorset containing a plurality of elements.

FIG. 3B is a data flow diagram of one embodiment of a traditional(legacy) matched filter with fault detection and a reference vector setcontaining a plurality of elements.

FIG. 4A is a data flow diagram of one embodiment of the presentinvention as a fault-aware matched filter with a reference vector setcontaining one element.

FIG. 4B is a data flow diagram of one embodiment of the presentinvention as a fault-aware matched filter with a reference vector setcontaining a plurality of elements.

FIG. 4C is a data flow diagram of one embodiment of the presentinvention as a fault-aware matched filter where the data paths aredynamically selected (i.e. during filter operation) instead of at designtime.

FIG. 5A is a bar graph representation of an example match statisticvector with no localization (MSV) according to one embodiment of thepresent invention.

FIG. 5B is a bar graph representation of an example 1-D match statisticvector with localization (MSM) according to one embodiment of thepresent invention.

FIG. 6 is a data flow diagram of one embodiment of a “generic” opticalflow algorithm.

FIG. 7 is a data flow diagram of one embodiment of the present inventionas a fault-aware optical flow.

FIG. 8 shows a graphical representation of the image input and vectorfield output from a realized embodiment of a fault-aware optical flowalgorithm. Three outputs are shown with varying output generatingbehavior that will be explained in the Detailed Description section.

DETAILED DESCRIPTION

Embodiments of the present invention include methods and systems whichcan provide a significant improvement in fault-detection capability whencompared to the current state-of-the-art for each respectivemethod/system. A method or system that implements an embodiment of amatched filter according to the present invention is referred to hereinas a “fault-aware matched filter.” An optical flow method or system thatimplements embodiments of the present invention is referred to herein asa “fault-aware optical flow” method or system.

The term “fault-aware” is used herein to refer to inventive embodimentsfor the detection of signal processing faults for a matched filter oroptical flow. Embodiments of the fault-aware matched filter andfault-aware optical flow disclosed herein may be used as augmentationsto traditional matched filters or optical flow techniques. For example,when embodiments of the present invention are used to augment acorrentropy matched filter, the result is a fault-aware correntropymatched filter.

Many conventional algorithms with built-in fault detection exist. FIG. 1shows a generic data flow diagram for an algorithm with built-in faultdetection. In FIG. 1, the generic algorithm 1 has a set of inputs 2 andsome output 4 that will be used as an input by the generic algorithm'soutput consumer. In addition, a set of fault-warnings 5 is present as anextra output. The internal processing 3 of the generic algorithm 1 isresponsible for generating both the output and fault warnings 5.

The term “confidence estimates” as used herein describes fault warningswith special properties related to embodiments of the invention. Adistinguishing characteristic of embodiments of the present invention isthat the confidence estimates for a fault-aware matched filter orfault-aware optical flow embodiment are derived such that the algorithmoutput and the failure modes for the confidence estimates do notsignificantly overlap. Additionally, embodiments of the fault-awarematched filter and fault-aware optical flow algorithms may be capable ofutilizing inputs from supplemental sensors to improve the reliability ofboth the output and the confidence estimates. For innovative embodimentsthat elect to support supplemental sensors, these sensors may berequired or they may be supported on an as-available basis. However,supplemental sensor inputs are not a requirement of the presentinvention.

Confidence estimates produced by embodiments of the present inventionmay, for example, include Boolean and/or numeric values. Booleanconfidence values provide a direct assessment of various fault/no-faultstates to the output consumer. Numeric confidence estimates leave theinterpretation to the output consumer, and are useful when multipleconsumers are present with differing input requirements. Note that thesesame rules apply to fault warnings from non-embodiments of the matchedfilter. Essentially, confidence estimates are fault warnings withdifferent numeric or Boolean statistics such that the aforementionedfailure mode property is observed.

Before describing embodiments of the present invention in detail, firstconsider the possible outcomes when an output is generated for a genericalgorithm with fault detection. The algorithm's output consumer mayeither accept or reject the output based on the confidence estimates.The possible outcomes for using a generic algorithm with fault-detectionmay be divided into four categories, ranked in order of decreasingdesirability.

-   -   1. The output is correct (output non-fault) and the        fault-detection is not triggered (fault-detection non-fault).    -   2. The output is incorrect (output fault) and the        fault-detection is triggered (fault-detection non-fault).    -   3. The output is correct (output non-fault) and the        fault-detection is triggered (fault-detection fault).    -   4. The output is incorrect (output fault) and the        fault-detection is not triggered (fault-detection fault).

Category 1 represents ideal operation for an algorithm with faultdetection. It is also ideal for cases when an algorithm with nofault-detection does not experience an output fault. The output of a(hypothetical) perfect algorithm that never performed out-of-spec andnever experienced any fault-detection “false alarms” (i.e.fault-detection fault) would always be classified in Category 1.However, real-world systems do not always work perfectly.

Category 2 represents the next best scenario. In Category 2, an outputfault has occurred, but it has been correctly detected by thefault-detection module. When a fault is signaled, the output consumer isexpected to ignore the output at a minimum. If the output consumerprovides error correction capability (or if such is built into thesubject algorithm), then it is (correctly) triggered in the scenario ofCategory 2. This category is unique to algorithms with fault detection.It occurs when the fault detection is triggered but is working properly.Without fault detection, the cases from this category would insteadbecome part of Category 4.

Category 3 contains cases where fault-detection methods incorrectlysignal that the output is in a fault state (i.e. a fault-detection faulthas occurred). The effect on the output consumer is the same as forCategory 2, except, in this case, the result is wasted data and wastedpost-processing (when error correction is erroneously triggered). Forcases in this category, the fault detection is responsible for“breaking” the algorithm. However, although data and processing may bewasted, no undetected out-of-spec output is generated. (The loss of datamay cause out-of-spec output, but the condition can be detected, sincethe data loss is known by the algorithm.)

Category 4 is the worst-case scenario for any generic algorithm withbuilt-in fault detection. It is also the default behavior when an outputfault occurs for an algorithm with no fault-detection. In this case, theoutput and the fault-detection method are in simultaneous fault states.The result is undetected out-of-spec behavior by the subject algorithm.If the output consumer is unable to independently detect the failure(which is usually computationally intensive and possibly significantlyless reliable), then the fault can propagate throughout the system'sentire signal processing pipeline. Such faults can range in severityfrom minor (e.g., a single bit error in a cellular communication system)to catastrophic (e.g., a mid-air collision between a UAV and anothervehicle or structure).

Algorithms without fault detection are “all-or-nothing”; use caseseither fall into Category 1 (best) or Category 4 (worst). There is nomiddle ground. However, for applications where there are no significantnegative consequences of undetected output faults, an algorithm withoutfault detection can be used with no reservations. An example of thistype of application is one where a human is the real decision maker, andis available to “clean up” the algorithm's mistakes. In suchapplications the algorithm's only real purpose is to reduce the workloadfor humans. Many algorithms developed by academia and used innon-real-time applications fall into this category.

Algorithms with fault detection that are not fault-aware according tothis invention have use cases that fall into all four categories.However, these algorithms exhibit an increased probability of Category 4“dual faults” relative to the fault-aware inventive methods because thealgorithm outputs and the fault warnings have substantially overlappingfailure modes. However, the consequences of Category 4 use cases are notthe same for all applications. A real-time non-critical applicationcould be an example of a use case where traditional fault detection is“good enough”. For this type of application, there is not time for ahuman to “clean up” huge amounts of mistakes, but the fault detectionmay be “good enough”. Many generic real-time algorithms developed byacademia or industry that never leave a controlled lab environment fallinto this category.

Certain fault-aware embodiments of the present invention may exhibit oneor more orders-of-magnitude fewer use cases that fall into Category 4.Because embodiments of the fault-aware techniques described hereinprovide confidence estimates that do not share all of the same failuremodes as the fault-aware output, the probability of simultaneous outputand fault-detection faults is statistically decreased because fewercommon failure scenarios exist. This is of the most significance forreal-time autonomous mission- and safety-critical applications, in whichthere is no time for a human to “clean up” mistakes and the consequencesof undetected mistakes can be catastrophic.

The following disclosure provides a detailed description of embodimentsof: (1) a fault-aware matched filter and fault-aware optical flow; and(2) innovative optical flow techniques with novel properties which areextremely well suited for fault-aware augmentation (including thebaseline and fault-aware versions).

A matched filter measures similarity between a data vector and anotherentity such as a function or reference vector set. In general, the datavector represents the input signal with superimposed noise. Thereference vector set consists of one or more elements for whichsimilarity is to be measured with respect to the data vector.Alternately, the reference vector set may be defined by a function suchthat it is not implemented as an actual data input to the system; forexample, the function or functions defining the reference vector set maybe implemented within the matched filter using any appropriatecomponent(s).

FIG. 2 (i.e., FIGS. 2A and 2B) and FIG. 3 (i.e., FIGS. 3A and 3B) showembodiments of a traditional matched filter without and withfault-detection for cases where a reference vector input set is presentand contains both one and a plurality of elements. The matched filterwith a single-element reference vector input set is shown in FIG. 2A. InFIG. 2A, the matched filter 6 has four possible inputs: a single datavector 7, a single reference vector 8, a region of interest (ROI) 9, anda set of filter configuration parameters 10. The ROI 9 and configurationparameters 10 are optional. Additionally, the reference vector 8 can beremoved in the case where the data vector 7 is referenced to a function.The matched filter output 11 is an example of a “legacy” or “underlying”matched filter output, as those terms are used herein, and may includeone or more values of numeric and/or binary type. The first component ofthe generic matched filter embodiment, FilterA 12, represents a singlefilter or a bank of parallel and/or series filters implementing thecomputations required to derive the matched filter output 11. TheFilterA output 13 data block represents all outputs and intermediateproducts of interest derived during the FilterA block 12 (or passedthrough from inputs such as the configuration parameters). The finalprocessing step in a generic matched filter is an (optional)thresholding 14 operation based on the FilterA output data product 13.In general, the thresholding operation 14 could instead be otherfunctions, and the term “output transformation function” refers to anyfunction which may be used in place of the thresholding operation 14.The combination of FilterA 12, FilterA output 13, and the thresholdingbased on FilterA output 14 (or other output transformation function) isan example of a “matched filter”.

FIG. 2B extends the generic matched filter embodiment from FIG. 2A tocreate a generic matched filter with fault-detection capability 15 (alsonot an inventive embodiment). The matched filter inputs 16 in FIG. 2Bare the same as elements 7-10 in FIG. 2A. Likewise, the data flowdiagram for the matched filter output computation in FIG. 2B is the sameas that in FIG. 2A. The new fault warning output 17 to the filter inFIG. 2B is derived from the FilterA output 13 by block 18. Because thefault warning outputs 17 are derived from the same filter set used toproduce the matched filter output 11, the fault warning outputs 17 willexhibit all or nearly all of the same failure modes as the matchedfilter output 11. Although the matched filter embodiment of FIG. 2Bproduces “fault warnings” as part of a fault-detection capability, it isnot “fault-aware” as that term is used herein.

Referring to FIGS. 3A and 3B, embodiments of a conventional matchedfilter that extend the reference vector set to multiple elements areshown. The generic matched filter with multiple reference vectors 19(FIG. 3A) has the same inputs and outputs as the single element version6 of FIG. 2A, except for a reference vector set with more than one entry20. The match-statistic vector (MSV) 21 is an array of FilterA outputswhere one output exists for each data-reference pair. Therefore, the MSV21 provides a measure of the strength of the FilterA response betweenthe data vector 20 and each of the reference vectors 20, and is the datastructure that enables the filter 19 to select which of the multiplereference vectors 20 is the “best match”. The final processing block 22selects the match statistic vector entry with the maximum (strongest)similarity response and applies a threshold, if applicable. The matchedfilter output 23 includes information on which data/reference pair wasthe maximum response.

The generic matched filter with fault detection and multiple referencevectors 24 of FIG. 3B is a combination of FIG. 3A and FIG. 2B. Thematched filter 24 of FIG. 3B receives the same inputs 31 as the matchedfilter 19 of FIG. 3A. In FIG. 3B, the location and magnitude of themaximum MSV response is identified 25 and the maximum element of the MSV21 is selected 26, and the fault warning outputs 17 are derived from theselected response. Once the maximum-response FilterA output 26 isselected, matched filter output 23 and fault-warning output 17generation occurs just as in the single reference vector case of FIG.2B.

Referring to FIG. 4 (i.e., FIGS. 4A, 4B, and 4C), three embodiments of afault-aware matched filter according to the present invention aredepicted. FIG. 4A shows an embodiment of a single reference vectorfault-aware matched filter 28. The entirety of the matched filter 6 fromFIG. 2A can be seen in FIG. 4A, illustrating that the fault-awarematched filter 28 may be used to augment a traditional matched filter.FIG. 4A shows how supplemental sensors 29 may be integrated as a newinput; however, these are shown merely as an example and do notconstitute a requirement of the present invention. Output feedback isalso an option for the fault-aware matched filter 28, but this is notdepicted in FIG. 4A.

The confidence estimates 30 for all of FIG. 4 perform a function that isin some ways similar to the fault warning outputs 17 shown in FIGS. 2Band 3B; however, at least one of the confidence estimate outputs 30 inFIG. 4 has substantially different failure modes than the matched filteroutput, as previously described. The fault-aware matched filters 28 and33 of FIGS. 4A and 4B may provide their confidence estimate outputs 30to the respective filter's output consumer. Additionally, a fault-awarematched filter might derive a third “fallback” output (not shown in thefigures) from the supplemental sensor inputs 29 and/or filter feedback(not shown). The fallback output could be used by the output consumer ifthe confidence estimates 30 indicate that the matched filter output isin a fault state. However, since a “fallback” output is not alwayspossible or desirable, it is not required in embodiments of theinvention.

Note the possibility of a modification of the fault-aware matched filterwhere the output consumer processes the confidence estimates and selectsbetween the matched filter output and a “fallback” output. In thismodification, the fault-aware matched filter's output consumer forwardsthe selected output to its own output consumer.

Internally, the fault-aware matched filter embodiment of FIG. 4A addstwo new filter blocks: FilterB 31 and FilterC 32. FilterB 31 is a singlefilter or a bank of parallel and/or series filters implementing thecomputations required to derive the confidence estimates 30 from thematched filter inputs 16. FilterC 32 is a single filter or a bank ofparallel and/or series filters for the supplemental sensor inputs 29, asneeded to derive confidence estimate outputs 30 or any output. TheFilterC block 32 is only present if the supplemental sensor inputs 29are present. The FilterB block 31 must be present if it is the onlysource for the confidence estimate outputs 30 with substantiallydifferent failure modes than FilterA 12. If, however, FilterC 32 canmeet the confidence estimate requirement, then FilterB 31 may beomitted. In FIG. 4B, the confidence estimates 30 are derived from theFilterA output 13 and at least one of the FilterB output 78 and theFilterC outputs 79.

Referring to FIG. 4B, an embodiment of the fault-aware matched filterwith multiple reference vectors 33 is shown. In this embodiment, thematch statistic vector A 34, which is output from FilterA 12, can beused to meet the aforementioned confidence estimate requirement directlyif statistics other than the peak or a transformation of the peakresponse are computed from it. The description below will elaborate onhow this may be accomplished in a particular embodiment of the MSV 34.If the failure mode requirement for the confidence estimate outputs ismet by quantities directly derived from MSV-A 34, then both FilterB andsupplemental sensor FilterC signal paths are optional. Otherwise, if theembodiment of FIG. 4B cannot derive 80 suitable confidence estimates 30from MSV-A 34, then at least one of the FilterB and FilterC data flowpaths are required. In FIG. 4B, the confidence estimates 30 are derivedfrom the selected FilterA output 26 and at least one of the MatchStatistic Vector (A) 34, the Match Statistic Vector (B) 35, and theFilterC outputs 79.

The embodiment of FIG. 4B can also support output feedback and“fallback” output as described for the embodiment of FIG. 4A. These arenot shown in FIG. 4B, and are not required for the embodiment of FIG. 4Bto be considered an embodiment of a fault-aware matched filter.

Referring to FIG. 4C, an embodiment of a fault-aware matched filter thatselects filter data paths dynamically 36 is shown. In this embodiment36, Filter1 38 and Filter2 39 represent filter blocks in the same manneras FilterA 12 and FilterB 31. For example, Filter1 38 may be FilterA 12,and Filter2 39 may be FilterB 31. As another example, Filter1 38 may beFilterB 31, and Filter2 39 may be FilterA 12.

In the embodiment of FIG. 4C, a dynamic filter selector 37 maps the datafrom Filter1 38 and Filter2 39 to dynamic outputs 76 and 77 (which aredifferent from each other). The dynamic filter selector 37 may performany mapping of outputs 74 and 75 to outputs 76 and 77. For example, thedynamic filter selector 37 may map Filter1 output 74 to dynamic output76, and map Filter2 output 75 to dynamic output 77. Alternatively, thedynamic filter selector 37 may map Filter1 output 74 to dynamic output77, and map Filter2 output 75 to dynamic output 76. Furthermore, thesorting of Filter1 Output 74 and Filter2 Output 75 into separate setscould instead be done within the Dynamic Filter Output 37. In otherwords, all of the inputs could come into the Dynamic Filter Selector 37in a single set (e.g., from a single filter) and then be processed andsorted internally by the Dynamic Filter Selector.

The dynamic filter selector 37 performs its selection based on one ormore selection inputs. For example, as shown in FIG. 4C, one or more ofthe matched filter inputs 16 may act as selection inputs to the dynamicfilter selector 37. Additionally or alternatively, one or more of thesupplemental sensor inputs 29 may act as selection inputs to the dynamicfilter selector 37. The dynamic filter selector 37 may use its selectioninput(s) to choose which mapping to perform (i.e., of outputs 74 and 75to outputs 76 and 77) using any function.

The dynamic outputs 76 and 77 may then be used in the same way asFilterA Output 13 and FilterB Output 78, respectively, in thefault-aware matched filter embodiment 28 of FIG. 4A. The embodiment ofFIG. 4C enables the set of filters which constituted the FilterA andFilterB paths in FIG. 4A instead to be chosen dynamically duringoperation, instead of at the time of design. This embodiment issignificant because it allows the derivation of the matched filteroutput 23 and confidence estimate outputs 30 to be optimized byselecting the best filters for both jobs based on situationalinformation contained in the inputs.

Although the embodiment of FIG. 4C is only shown for the case of asingle-element reference vector input, it may perform the same functionfor multi-element reference vector sets through an extension similar tothat used to extend the embodiment of FIG. 4A to that of FIG. 4B. Also,the embodiment of FIG. 4C may support output feedback and “fallback”output, although these are not shown in FIG. 4C.

An example embodiment of a conventional matched filter with faultdetection and multiple reference vectors (FIG. 3B) is the matched filterused to derive the optical flow in the KLT algorithm (Lucas and Kanade1981; Tomasi and Kanade 1991; Shi and Tomasi 1994). In the KLT opticalflow algorithm, a single optical flow sample is computed from agradient-based product that is derived by comparing a reference imagetemplate (data vector) and possible matches within a region of interestin a larger image (reference vector set). The algorithm is processediteratively such that the location of the template in the referencevector set converges (hopefully) towards the maximum response. When theoutput position displacement changes by less than a specified value(configuration parameter), then the algorithm breaks and the output isselected. The MSV in this example is the set of all data at eachiterative step. In KLT, the flow sample is discarded if the finalgradient product is less than some specified threshold. However, themagnitude of the gradient product is also used to determine the outputlocation during each iterative pass.

The key observation from this example is that the matched filter used bythe KLT algorithm derives both the output and failure warnings from thesame numeric value: the gradient peak. The thresholding operationprovides a method for interpreting the numeric data as Booleanfault/no-fault, but it does not provide substantially different failuremodes than the raw gradient value used to generate the output.

Embodiments of the match statistic vector 21 will now be described. Thematch statistic vector 21 (and other match statistic vectors disclosedherein) may represent a variable dimensional (e.g., 1-D, 2-D, etc.)measure of similarity between the data vector 7 and each of a pluralityof reference vectors 20. As a result, the match statistic vector 21 mayhave N entries, where N is the number of reference vectors 20. The matchstatistic vector entry #i (denoted as MSV[i]) contains information onthe strength of the match between the data vector 7 (denoted DV) andreference vector #i (denoted RV[i]). In other words, MSV[i] is a measureof similarity between DV and RV[i]. Additionally, it is desirable (butnot always absolutely necessary) to express the match statistic vectorsimilarity 21 in a normalized form such that 0.0 represents an idealnon-match and 1.0 represents an ideal (exact) match between DV andRV[i]. This normalized form is called “normalized positive match atunity” or NPMU form.

There are two separate classes of match statistic vector. The class ofmatch statistic vector is determined by the filter's application. Thematch statistic vector classes are: (1) localized, and (2)non-localized. A localized match statistic vector, also called a matchstatistic map, is one in which the order of elements has some meaning(and is usually part of the desired output). Since the arrangement ofthe entries of a localized match statistic vector has meaning, thesevectors can be either one- or multi-dimensional. In contrast, the orderof a non-localized match statistic vector has no meaning. Non-localizedvectors are usually 1-D, since no additional benefit is gained fromhigher-dimensional representations. From this point on, the term matchstatistic map (MSM) will be used to refer to a localized match statisticvector. In the context of an MSM, the term match statistic vector (MSV)will refer exclusively to the non-localized class. MSV will also be usedas a general term where no context is specified.

The difference between an MSV and an MSM can be more clearly explainedwith usage examples. An example of a matched filter application thatwould require an MSV is the recognition of faces within an image. Inthis example, the goal of the filter is the comparison of a picture ofan individual's face (the data vector) to other faces in an unordereddatabase (the reference vector). Since the database is unordered, thereis no meaning associated with the index of each record within thedatabase. Therefore, this application would use an MSV. However, amatched filter application that finds faces within an image (any face .. . not specific faces) would require an MSM, because the position(localization) of the face in the input image is the output product.Therefore, an end-user system that finds and identifies faces within animage would require two fault-aware match filters. The filter to findthe faces would contain an MSM, and the filter to identify the faceswould contain an MSV.

This example references one possible embodiment of the fault-awarematched filter 33 shown in FIG. 4B. In this example, the matched filteroutput 23 provides the answer to the question, “Which of the referencevector inputs, if any, is the best match for the data vector input?”

The confidence estimate outputs 30 may, for example, representquantitative answers to the following questions:

-   -   1. “How strong is the match between the data vector and the        selected reference vector?”    -   2. “How much stronger is the selected match compared to the rest        of the reference vectors?”    -   3. (Localized only) “Does the spatial accuracy of the output        meet the system spec?”

Since the quantitative measure associated with question 1 is required inorder to select the best match as the filter's output, this quantitativemeasure represents an instance of FilterA Output that is used to deriveboth the filter output 23 and the confidence estimates 30. In fact, thissame quantitative measure is routinely used to derive fault warnings ingeneric (i.e. non-inventive embodiments) of a matched filter (ex: thematched filter within the KLT algorithm).

Quantitative measures associated with questions 2 and 3 are not directlyused to derive the output. They can be derived directly from MSV-A 34and are useful as confidence estimates that exhibit substantiallydifferent failure modes with respect to the quantitative metric ofquestion 1 (which is used to produce the output). When the confidenceestimates 30 include the answers to questions 2 and 3, then the filter33 of this example is an embodiment of a fault-aware matched filter, asthat term is used herein. Additionally, because confidence estimates 30with substantially different failure modes as the output 23 have beenderived directly from MSV-A 34, the FilterB and FilterC data flow pathsare both entirely optional for this example embodiment. (Remember thatthis is only an option when multiple reference vectors are present).

Quantitative metrics will now be described that can be derived from anMSV or MSM which are examples of confidence estimates 30. These are notguaranteed to be optimal for any application; however, they are observedto be useful for optical flow applications of fault-aware matchedfilters implemented according to embodiments of the present invention.

Referring to FIG. 5A, an example MSV bar graph 500 is shown according toone embodiment of the present invention. The bar graph 500 in FIG. 5Ashows important quantities from the MSV that can be used to derive onepossible (useful) set of confidence estimates 30. This example is an MSVof length nine, implying the reference vector input set has nineelements. In the embodiment represented by FIG. 5A, no localizationinformation is encoded in their order. The largest peak 42 in theexample MSV occurs in bin two and has a magnitude of PK1 40. Therefore,the filter output (FIG. 1A) would select reference vector input setelement #2 as the output. Since the elements in the MSV represent matchstrengths between the data vector and each reference vector, PK1 40 maybe referred to herein as the “peak match strength.”

The second largest peak 43 occurs in bin four and has a magnitude of PK241. This peak represents the non-selected reference vector with the mostsimilarity to the data vector (i.e., the second-highest of the matchstrengths in the MSV). AVG 44 is the average of all peaks, and DEV 45 isthe standard deviation of all peaks (both computations can optionallyomit the peak magnitude, PK1).

The most obvious confidence estimate is the absolute MSV peak, PK1 40,since it directly answers question 1, above. However, because thismetric was directly used to select the example matched filter output,the output has identical failure modes to this confidence estimate. PK140 is allowed as a confidence estimate, but since it does not meet thepreviously discussed requirement regarding confidence estimate failuremodes, it cannot be the only confidence estimate. Note: all knownmatched filters with multiple reference vectors use the MSV peak toselect the output. They may use dramatically different terminology todescribe what they are doing, but the output is always selectedaccording to the “best” response, which is the MSV peak in this example.Because the peak magnitude is available, it is frequently used for faultdetection by state-of-the-art embodiments (again, the matched filter inthe KLT optical flow algorithm is an example of this assertion).

Note that transformations including but not limited to thresholding andre-scaling can be applied to the peak magnitude PK1 40 (thresholding isthe most popular by far). No transformation exists that can achieve thelevel of error rejection desired for use with the fault-aware matchedfilter using PK1 40 alone. Therefore, confidence estimates for afault-aware matched filter embodiment cannot be generated fromtransformations to the peak magnitude PK1 40 alone or in combinationwith other operations only involving PK1 40.

Another quantitative metric derivable from the MSV is the ratio PK1/PK2.Qualitatively, the PK1/PK2 ratio is a measure of how close the next bestoption is to the selection; this ratio can be used to answer question 2,above. In practice, when the input data vector is corrupted by certaintypes of noise (e.g. uniform, Gaussian, etc.), the peak magnitude PK1 40will decrease. However, PK2 41 will decrease by almost the samepercentage, so that the PK1/PK2 ratio will remain nearly unchanged. Thismetric is far less sensitive to the specified types of noise and doesnot share them as a failure mode with the output derived from PK1 40.Since the listed noise types are significant for nearly any type ofsystem, inclusion of the PK1/PK2 ratio does meet the requirement forselecting confidence estimates, and an embodiment using thisquantitative output is a fault-aware matched filter.

Other quantitative metrics for testing question 2 are summarized inTable 1. All of these metrics are valid for both localized andnon-localized MSV, and other metrics are possible in addition to thoselisted. Note that k in Table 1 is a predetermined constant value.

TABLE 1 Confidence Metrics (MSV & MSM) Name Formula Absolute peakmagnitude PK1 Similarity ambiguity ratio PK1/PK2 Similarity ambiguitydifference PK1 − PK2 Peak to mean ratio PK1/AVG Peak to mean differencePK1 − AVG Peak to deviated mean ratio PK1/ (AVG + k*DEV) Peak todeviated mean difference PK1 − (AVG + k*DEV)

Referring to FIG. 5B, an example MSM bar graph 550 is shown according toone embodiment of the present invention. The MSM bar graph 550 in FIG.5B shows important quantities from an MSM that can be used to derivesome possible metrics that make up the confidence estimate outputs 30when localization information exists in the reference vector set. Theexample MSM in FIG. 5B may, for example, represent the result of a 1-Dtemplate search operation, where bin j is adjacent in the search windowto bins (j−1) and (j+1). The length of the example MSM illustrated inFIG. 5B is nine, implying there are nine ordered reference data sets.The largest peak in the example MSM occurs in bin two.

Additional metrics may be identified based on the encoded orderinformation (localization). An example metric that can be derived from amatch statistic map with encoded order information is one that addressesquestion 3 from paragraph 0070. The idea of spatial accuracy can berepresented in the MSM by the concept of a “local radius,” representedby RAD 46 in FIG. 5B. The local radius represents the positional marginof uncertainty allowed by the application in quantized bin units (orpixels for images). It may have any non-negative value. The example inFIG. 5B shows a local radius RAD=1. The two peaks closest to PK1 andoutside of the region defined by the local radius are used to test thepositional margin of uncertainty with respect to the local radius. Inthis example, these are bins 0 and 4. Of these, the maximum, PK_L 47, isfound in bin 4. In other words, PK_L is the higher (maximum) of the twomatch strengths MSM[j+RAD+1] and MSM[j−RAD−1], where j is the index ofthe peak match strength.

Table 2 lists confidence metrics that may be derived from the example toanswer question 3, above. As with the previous example, this list is notall-inclusive and is not optimized for any application.

The first two metrics in Table 2 measure the relative strength betweenthe absolute peak and the local similarity peak. The last two metricsmeasure the position of the absolute peak with respect to the MSMboundary. Tests with respect to the boundary are relevant because peakslocated near the bounds of the MSM are inherently less reliable thanthose located near the center.

TABLE 2 Confidence Metrics (MSM Only) Assertions Name Formula TestedLocal similarity ratio PK1/PK_L 3 Local similarity PK1 − PK_L 3difference Border uncertainty #1 (index(PK1) > 0) 3 AND (index(PK1) < N− 1) Border uncertainty #2 (index(PK1) > RAD) 3 AND (index(PK1) < N −RAD − 1)

As described above, the confidence estimate outputs 30 may be derivedfrom any combination of one or more of the metrics disclosed herein. Inparticular, it may be useful to derive the confidence estimates from acombination of the peak match strength PK1 40 and at least one metricdrawn from Tables 2 and 3.

An optical flow algorithm is a method for measuring the apparent vectordisplacement between images in a sequence. When images in the sequenceare not acquired at the same time, the vector displacement alsocorresponds to an apparent velocity in image space. Otherwise, thevector displacement corresponds to a difference in instantaneous camerapose. The image sequence set requires a minimum of two images, althoughany greater number of images can be used. The act of measuring theoptical flow for an input image sequence with more than two images isperformed by daisy-chaining the optical flow operation for two pairsacross all sequential image pairs in the input sequence. Because this isa trivial extension for those skilled in the art, the exampleembodiments in this disclosure are limited to two input images tominimize complexity in the drawings.

The optical flow output defines a coregistration between the images inthe sequence (e.g., between the first image input 49 and the secondimage input 50), where the act of coregistration provides theinformation necessary to “undo” or “reverse” the displacement betweenthe images at the sample locations. Algorithms exist to estimate thecoregistration for the entirety of all input images from a limited setof optical flow samples; these algorithms are examples of consumers foroptical flow output.

Optical flow systems include matched filters or other components whichperform the same functions as a matched filter, and these components area significant portion of the complexity. In general, at least onematched filter operation (i.e., an operation performed by a matchedfilter or its functional equivalent) is performed per optical flowsample. There is no standardized matched filter used for optical flow;state-of-the-art optical flow algorithm development requires customfilters.

Referring to FIG. 6, a representative embodiment of a data flow diagramfor a generic optical flow processor 48 (prior art) is shown. The inputset to the processor 48 consists of a “first” image input 49 and a“second” image input 50, where the “second” image input has a relativeposition subsequent to the “first” image input in the image inputsequence. The optical flow configuration input 51 is the set of allconfiguration and control parameters required by the optical flowprocessor 48. The output set to the processor 48 consists of an opticalflow primary output 52 and an optical flow secondary output 53. Theprimary output 52 is an array of velocity vectors spatially registeredto the input images 49 and 50. The secondary output 53 can consist ofany other secondary quantities that are required as output for aparticular embodiment of an optical flow processor. Secondary outputexamples include but are not limited to acceleration data or copies ofany output of the matched filters used to derive each output sample.

The first step in the derivation of the optical flow output set in theembodiment of FIG. 6 is application of a frame sample filter 54 to thefirst image input 49. The frame sample filter 54 implements selection ofthe desired sample pattern for the optical flow field. Many ways foraccomplishing this exist, such as the method of Shi and Tomasi 1994which identified “good features to track”. If the frame sample filter 54is implemented as an embodiment of Shi and Tomasi's method then it willconsist of, at a minimum, pre-processing filters (e.g. low-pass) and anarray of matched filters. The output set of these matched filters is anarray of sample coordinates sorted according to the strength of thematched filter response (equivalent to PK1 40 from FIG. 5A). The filtersare selected according to the N strongest filter responses above athreshold, resulting in between 0 and N flow samples in the resultingsample pattern 55. Note that other methods exist for implementing theframe sample filter 54. The simplest possible method is a static samplepattern (e.g. grid) that is specified at filter design time. In thiscase, the frame sample filter 54 does not contain a matched filter ofany type. Therefore, integration of a matched filter into the framesample filter 54 is not required for the optical flow processor 48.

The next step in the derivation of the optical flow output set in theembodiment of FIG. 6 is application of a sample registration filter 56to the second image input 50. In this step, the second input image 50 isprocessed to find the locations of the “best match” for each sample inthe sample pattern 55. This step may include arbitrarily complicatedpre- and post-processing, but the act of identifying the “best match”for each sample typically is performed by a matched filter with faultdetection. Any useful conventional optical flow algorithm wouldimplement the matched filter with fault detection of FIG. 3B such thatthe matched filter output 23 generates the set of unfiltered flowsamples 57 and the matched filter fault warning outputs 17 generates thesample fault warnings 58. The unfiltered flow samples 57 are a set ofoptical flow samples such that each element (called an “optical flowsample”) in the set defines a coregistration between first image input49 and second image input 50. The designation “unfiltered” for theunfiltered flow samples 57 does not imply that the unfiltered flowsamples 57 were not produced using a filter, but rather that flowsamples which are in-fault have not yet been removed from the unfilteredflow samples 57. The sample fault warnings 58 are a set of quantitativemeasures corresponding to each optical flow sample representing a degreeof confidence in the coregistration. Additionally, the sample faultwarnings can be used to identify which, if any, of the elements of theunfiltered flow samples 57 are in-fault.

Note that the process of creating the coregistration also creates thedisplacement and/or velocity vector output that are the primary outputof an optical flow algorithm.

Next, the flow sample fault filter 59 will process the unfiltered flowsamples 57 and the sample fault warnings 59 to remove any flow samplefaults signaled in the sample fault warnings 58. The flow sample faultfilter is the optical flow algorithm component that is the first outputconsumer of the matched filter in the sample registration filter 56. Theoutput of the flow sample fault filter 59 is a set of filtered flowsamples 60 such that between zero and N of the original N flow fieldsamples in the sample pattern 55 remain. In contrast with the unfilteredflow samples 57, the designation “filtered” for the filtered flowsamples 60 means that flow samples which are in-fault have been removedfrom the filtered flow samples 60.

The final step is to generate secondary outputs 61, if any, and tooutput all elements of the output set 52-53. Note that, althoughconventional optical flow algorithms may elect to forward any faultwarnings from the matched filters to the output consumers via thesecondary output 52, these fault warnings are not used by the opticalflow algorithm to assess the suitability of its output with respect tothe requirements.

A critical limitation of the state-of-the-art optical flow is that noreliable method exists for dynamically detecting the presence of a flowfield fault (not to be confused with a flow sample fault . . . seeparagraph 0016). Any method for detecting a flow field fault should nothave significantly overlapping failure modes with the optical flowprimary output 53 for the same reasons discussed for the fault-awarematched filter. Unfortunately, this need is not met by the act ofrecording sample fault warnings 58 in the optical flow secondary output52 according to the embodiment description for FIG. 6. This is becausethe optical flow primary output 53 is essentially derived from thematched filter output 23 while the sample fault warnings 58 are derivedfrom the matched filter's fault warning output, and the two outputs fromthe matched filter 24 of FIG. 3B have significantly overlapping failuremodes.

Embodiments of the present invention for computing fault-aware opticalflow address the limitation of the current state of the art through fourembodiments that will now be described. Each such embodiment is avariation on the fault aware optical flow 62 embodiment shown in FIG. 7.The input and output sets for the example embodiment shown in FIG. 7include some of the same inputs as those shown in FIG. 6 (namely, thefirst image input 49, the second image input 50, and the optical flowconfiguration input 51), but may also include additional inputs. Forexample, the input set for the fault-aware optical flow processor 62 ofFIG. 7 includes supplemental sensor inputs 63. These (optional)supplemental sensor inputs 63 are utilized by the frame sample filter 66and/or the sample registration filter 67 for both predictive andfault-reduction means. The data for the supplemental sensor input 63includes data sourced from hardware with differing failure modes thanthe imager used to acquire the input images 49-50. One exampleembodiment of fault-aware optical flow might elect to use data fromaccelerometer and rate gyros as supplemental sensor inputs 63.

The output set of FIG. 7 includes the optical flow secondary outputs 52and optical flow primary output 53 of FIG. 6, but also includes opticalflow confidence estimate outputs 64 that are derived by an optical flowconfidence estimate generator 65. The optical flow confidence estimategenerator 65 produces the confidence estimate outputs 64 from at leastone of the supplemental sensor inputs 63, the optical flow configurationinput 51, the sample fault warnings 68, and the filtered flow samples60. In an embodiment of fault-aware optical flow, the frame registrationfilter 66 and the sample registration filter 67 may contain fault-awarematched filters (e.g., of the type shown in FIG. 4B or FIG. 4C). If thesample registration filter 67 contains a fault-aware matched filter,then the sample fault warnings 68 are confidence estimates for thematched filter output according to the definition provided above inconnection with the fault-aware matched filter.

One embodiment of fault-aware optical flow utilizes supplemental sensorinputs 63 for output prediction and fault detection for one or both ofthe frame sample filter 66 and the sample registration filter. In thisinventive embodiment, the frame sample filter 66 and the sampleregistration filter 67 can integrate fault-aware matched filters or theycan be as described for the embodiment of FIG. 6. In this embodiment,the optical flow confidence estimate generator 65 and the associatedoptical flow confidence estimate outputs 64 are optional.

In other embodiments of fault-aware optical flow, the supplementalsensor inputs 63 are optional. A second embodiment of fault-awareoptical flow utilizes fault-aware matched filters in the sampleregistration filter 67. The fault-aware matched filter may optionally beincluded as part of the frame sample filter 66. In this embodiment offault-aware optical flow, the fault detection performance of the flowsample fault filter 59 will be improved because the sample faultwarnings 68 are embodiments of fault-aware matched filter confidenceestimates 30. In this embodiment, no optical flow confidence estimateoutputs 64 are generated.

In a third embodiment of fault-aware optical flow, the frame samplefilter 66 and sample registration filter 67 include the fault-awarematched filter in the manner described above, and the optical flowconfidence estimate outputs 64 are also present. In a fourth embodiment,the frame sample filter 66 and sample registration filter 67 do notinclude a fault-aware matched filter. For this embodiment, the opticalflow confidence estimate generator 65 and associated outputs 64 must bepresent.

When fault-aware optical flow is to be applied to machine vision forunmanned aerial vehicles, it may be useful for the matched filter tosatisfy particular requirements. In particular, certain benefits may beobtained by enabling a matched filter's MSM to 1) exhibit extremeselectivity, 2) provide noise-rejection for non-Gaussian error sources,and 3) provide computational complexity comparable to absolute error orRMS error metrics. In particular, these qualities may enable therejection of flow field faults corresponding to real-world events suchas camera out-of-focus issues and rapidly-varying solar effects. Nomatched filter traditionally used for optical flow meets these needs.

Embodiments of the present invention use a measure of similarity calledcorrentropy to provide these benefits to fault-aware optical flow. Inparticular, certain embodiments of the present invention use acorrentropy matched filter in which the correntropy exponential functionis implemented using a lookup table enhancement. Such an implementationprovides all three of the benefits listed above.

For example, embodiments of the present invention include optical flowtechniques referred to herein as “correntropy optical flow,” in whichthe FilterA 12 block of FIGS. 2A, 2B, 3A, and 3B is implemented in wholeor part using an embodiment of the correntropy matched filter.

As another embodiment, embodiments of the present invention includefault-aware optical flow techniques referred to herein as “fault-awarecorrentropy optical flow,” in which the correntropy matched filter isaugmented with one of the fault-aware matched filters disclosed herein.The fault-aware correntropy matched filter may be integrated into theframe sample filter 66 and/or sample registration filter 67 (FIG. 7)according to one of the two embodiments of fault-aware optical flow thatutilize a fault-aware matched filter.

Some possible embodiments for the optical flow confidence estimateoutputs 64 will now be described. The following discussion is not meantto be inclusive or to represent an “optimal” list for any application.Rather, this list represents an enabling disclosure for some confidenceestimates that are useful for some applications.

1. Flow sample count—The number of optical flow samples remaining in theoptical flow primary output 53 or within a region of interest is auseful metric. Some applications (e.g. 3-D motion estimation via opticalflow and structure-from-motion) require a minimum number of flowsamples. Since the flow sample fault filter 59 can dynamically removesamples at run time, there is no way to guarantee that the requiredsample counts will be present.

2. Flow sample density—The density of optical flow samples for a regionof interest. Visual targeting applications can use this metric as aconfidence estimate.

3. Flow magnitude—The magnitude of the flow in a region of interest (oraverage across the entire scene) is useful as a confidence estimate whendealing with severely quantized images. In these cases, the quantizationerror (or other error) for “short” length vectors can becomesignificant. The optical flow output specification may elect to declarethat flow vectors of insufficient length are indeterminate andindicative of a fault condition.

Other confidence estimates are possible; the set provided merelyrepresents three examples. Other optical flow confidence estimates mayinclude positional information or derive a “confidence map” from thefiltered flow samples and numeric fault warning (or fault-aware matchedfilter confidence estimate) data. The possible combinations are endless;however, the optical flow confidence estimates should possess failuremodes with significant differences compared to the optical flow output.

Referring to FIG. 8, a visual representation of the output of afault-aware correntropy optical flow field is shown for an imagesequence acquired from a forward-facing camera of a small unmannedaerial system. The top two images in FIG. 8 show the first 69 and second70 image inputs, which are functionally equivalent to the “first” imageinput 49 and “second” image input 50 of the fault-aware optical flowembodiment in FIG. 7. The difference between the images is difficult tosee by an untrained eye; however, the second input image 70 istranslated slightly down and rotated clockwise with respect to the firstinput image 69. This particular image pair is extremely challenging formodern optical flow algorithms for reasons that are not obvious to thenaked eye. First, a significant amount of motion blur is present due tothe wide field-of-view and motion of the aircraft. Second, the camera isslightly out-of-focus due to a rough landing on a previous flight.Third, a slight illumination variation exists across frames due toorientation variations of the camera with respect to the sun (barelydetectable by the naked eye in the sky region for high qualityreproductions; otherwise, not evident to the naked eye). Theillumination variation is the most severe issue, because it is a directviolation of a fundamental constraint for optical flow. When this occursin real-world scenes, the fundamental assumptions governing themeasurement of optical flow are violated and flow sample faults areguaranteed.

Regarding the visualization 71 of the fault-aware correntropy opticalflow algorithm's unfiltered flow samples depicted in FIG. 8, theobservation was made that significantly fewer outliers were present inthe unfiltered flow samples due to the superior performance of thecorrentropy matched filter. The flow sample faults circled in theunfiltered sample visualization 71 are significantly less in numbercompared to any other tested optical flow algorithm, including KLT.

This embodiment of fault-aware correntropy optical flow utilized afault-aware correntropy matched filter in the sample registration filter67. The fault-aware correntropy matched filter confidence estimates usedin this example were the simplest possible set that could be deriveddirectly from the match statistic map. The first estimate chosen was thepeak magnitude (PK1 40 from FIG. 5A). Although a useful metric, previousdiscussion clearly indicates that PK1 40 cannot be the only metricselected.

The second metric selected as a confidence estimate for the fault-awarecorrentropy matched filter embedded in the sample registration filter 67was the PK1/PK2 ratio. The PK1/PK2 ratio was introduced in paragraph0078 and named in Table 1 as the “similarity ambiguity ratio”. It wasobserved that the addition of this metric as a confidence estimateproduced greater than one order of magnitude of improvement in the rateof undetected flow sample faults without a significant increase in therate of “fault-detection faults”. An order of magnitude improvement,however, is not required; other significant improvements include, forexample, improvements of at least 10%, 50%, 100%, 200%, and 500%. Inother words, this metric allowed the flow sample fault filter 59 tothrow out nearly all flow sample faults without discarding a significantnumber of valid flow samples, measured over the course of a 30 secondflight video containing the source frames 69-70.

Even when the flow sample fault filter 59 applies what is qualitativelydescribed as “loose” filtering, the resultant optical flow vector fieldvisualization 72 shows that only one flow field fault remains in theentire two-frame sequence, while many valid flow samples are stillpresent. In contrast, the KLT algorithm (without the fault-awarecorrentropy matched filter) was forced to discard all of its samples toachieve the same result on this image sequence. (This is why no resultis shown for KLT; there was nothing left to show). Furthermore, if“strict” filtering is applied by the flow sample fault filter 59, thenall flow sample faults are removed 73, and good flow data remains. Noother optical flow algorithm in existence today is capable of achievingresults even close to that achieved in this example by this embodimentof fault-aware correntropy optical flow for similar computer resourceutilization.

The particular processing operations illustrated in FIGS. 2, 3, 4, 6,and 7 are merely examples and do not constitute limitations of thepresent invention. The same or similar results may be obtained in waysother than those shown in FIGS. 2, 3, 4, 6, and 7.

The techniques disclosed herein are particularly useful in conjunctionwith autonomous mission- and safety-critical embedded systems withstrict reliability requirements. The greatest known benefit is gainedwhen a system does not have a human in the loop to “correct” mistakes.Three examples of such systems are: (1) a vision-based sense-and-avoidsubsystem of a small unmanned aerial vehicle (UAV); (2) an automatedtarget identification/classification algorithm in a mine-hunting system,and (3) a traditional cellular communications system. These are merelyexamples, however, and do not constitute limitations of the presentinvention.

It is to be understood that although the invention has been describedabove in terms of particular embodiments, the foregoing embodiments areprovided as illustrative only, and do not limit or define the scope ofthe invention. Various other embodiments, including but not limited tothe following, are also within the scope of the claims. For example,elements and components described herein may be further divided intoadditional components or joined together to form fewer components forperforming the same functions.

The techniques described above may be implemented, for example, inhardware, software tangibly embodied in a computer-readable medium (suchas in the form of non-transitory signals stored in the computer-readablemedium), firmware, or any combination thereof. The techniques describedabove may be implemented in one or more computer programs executing on aprogrammable computer including a processor, a storage medium readableby the processor (including, for example, volatile and non-volatilememory and/or storage elements), at least one input device, and at leastone output device. Program code may be applied to input entered usingthe input device to perform the functions described and to generateoutput. The output may be provided to one or more output devices.

Each computer program within the scope of the claims below may beimplemented in any programming language, such as assembly language,machine language, a high-level procedural programming language, anobject-oriented programming language, or a hardware definition language(HDL). The programming language may, for example, be a compiled orinterpreted programming language.

Each such computer program may be implemented in a computer programproduct tangibly embodied in a machine-readable storage device forexecution by a computer processor. Method steps of the invention may beperformed by a computer processor executing a program tangibly embodiedon a computer-readable medium to perform functions of the invention byoperating on input and generating output. Suitable processors include,by way of example, both general and special purpose microprocessors.Generally, the processor receives instructions and data from a read-onlymemory and/or a random access memory. Storage devices suitable fortangibly embodying computer program instructions include, for example,all forms of non-volatile memory, such as semiconductor memory devices,including EPROM, EEPROM, and flash memory devices; magnetic disks suchas internal hard disks and removable disks; magneto-optical disks; andCD-ROMs. Any of the foregoing may be supplemented by, or incorporatedin, specially-designed ASICs (application-specific integrated circuits)or FPGAs (Field-Programmable Gate Arrays). A computer can generally alsoreceive programs and data from a storage medium such as an internal disk(not shown) or a removable disk. These elements will also be found in aconventional desktop or workstation computer as well as other computerssuitable for executing computer programs implementing the methodsdescribed herein, which may be used in conjunction with any digitalprint engine or marking engine, display monitor, or other raster outputdevice capable of producing color or gray scale pixels on paper, film,display screen, or other output medium.

1. A method comprising: (A) applying a first filter to a data vector anda set R, the set R containing at least one reference vector, to producefirst filter output representing a measure of similarity of the datavector to at least one reference vector in the set R; (B) applying asecond filter to second filter inputs to produce second filter output,wherein the first filter has at least one failure mode not shared by thesecond filter; (C) deriving, from the first filter output and the secondfilter output, confidence estimate output representing a degree ofconfidence in the correctness of the first filter output.
 2. The methodof claim 1, further comprising: (D) applying an output transformationfunction to the first filter output to produce a third filter output,wherein the confidence estimate output represents a degree of confidencein the correctness of the third filter output; and (E) providing thethird filter output and the confidence estimate output as output.
 3. Themethod of claim 1, wherein the first filter inputs are the same as thesecond filter inputs.
 4. The method of claim 1, wherein the secondfilter inputs are disjoint from the first filter inputs.
 5. The methodof claim 1, wherein the second filter inputs include both the firstfilter inputs and additional filter inputs not contained within thefirst filter inputs.
 6. The method of claim 1, wherein the set Rcontains a plurality of reference vectors, and wherein the first filteroutput comprises a first plurality of measures of similarity specifying,for each reference vector V in the set R, a strength of a match betweenthe data vector and the reference vector V, and wherein (C) comprisesselecting a first one of the first plurality of measures of similarityand deriving the confidence estimate output from the first one of thefirst plurality of measures of similarity and the second filter output.7. The method of claim 6, wherein the set R contains a plurality ofreference vectors, and wherein the second filter output comprises asecond plurality of measures of similarity specifying, for eachreference vector V in the set R, a strength of a match between the datavector and the reference vector V, and wherein (C) comprises selecting asecond one of the plurality of measures of similarity and deriving theconfidence estimate output from the first one of the first plurality ofmeasures of similarity and the second one of the second plurality ofmeasures of similarity.
 8. The method of claim 6, wherein (C) comprises:(C)(1) identifying a peak match strength PK1 among the first pluralityof measures of similarity; (C)(2) identifying a second-highest matchstrength PK2 among the first plurality of measures of similarity; (C)(3)identifying a quotient PK1/PK2; and (C)(4) identifying the confidenceestimate output based on PK1 and PK1/PK2.
 9. The method of claim 6,wherein (C) comprises: (C)(1) identifying a peak match strength PK1among the first plurality of measures of similarity; (C)(2) identifyinga second-highest match strength PK2 among the first plurality ofmeasures of similarity; (C)(3) identifying a difference PK1-PK2; and(C)(4) identifying the confidence estimate output based on PK1 andPK1-PK2.
 10. The method of claim 6, wherein (C) comprises: (C)(1)identifying a peak match strength PK1 among the first plurality ofmeasures of similarity; (C)(2) identifying an average AVG of the firstplurality of measures of similarity; (C)(3) identifying a quotientPK1/AVG; and (C)(4) identifying the confidence estimate output based onPK1 and PK1/AVG.
 11. The method of claim 6, wherein (C) comprises:(C)(1) identifying a peak match strength PK1 among the first pluralityof measures of similarity; (C)(2) identifying an average AVG of thefirst plurality of measures of similarity; (C)(3) identifying adifference PK1-AVG; and (C)(4) identifying the confidence estimateoutput based on PK1 and PK1-AVG.
 12. The method of claim 6, wherein (C)comprises: (C)(1) identifying a peak match strength PK1 among the firstplurality of measures of similarity; (C)(2) identifying an average AVGof the first plurality of measures of similarity; (C)(3) identifying astandard deviation DEV of the first plurality of measures of similarity;(C)(3) identifying a quotient PK1/(AVG+k*DEV), wherein k is a constant;and (C)(4) identifying the confidence estimate output based on PK1 and(PK1/(AVG+k*DEV)).
 13. The method of claim 6, wherein (C) comprises:(C)(1) identifying a peak match strength PK1 among the first pluralityof measures of similarity; (C)(2) identifying an average AVG of thefirst plurality of measures of similarity; (C)(3) identifying a standarddeviation DEV of the first plurality of measures of similarity; (C)(3)identifying a difference PK1−(AVG+k*DEV), wherein k is a constant; and(C)(4) identifying the confidence estimate output based on PK1 and(PK1−(AVG+k*DEV)).
 14. The method of claim 6, wherein MSV[i] indicates amatch strength at index i within the match strength vector, wherein R[i]indicates a reference vector at index i within the plurality of orderedreference vectors, and wherein (C) comprises: (C)(1) identifying a peakmatch strength PK1 among the first plurality of measures of similarity,wherein PK1 is the match strength of MSV[j]; (C)(2) identifying a matchstrength PK_L, wherein PK_L is the maximum of match strengthsMSV[j+RAD+1] and MSV[j−RAD−1], for a predetermined value of RAD; (C)(3)identifying a quotient PK1/PK_L; and (C)(4) identifying the confidenceestimate output based on PK1 and PK1/PK_L.
 15. The method of claim 6,wherein MSV[i] indicates a match strength at index i within the matchstrength vector, wherein R[i] indicates a reference vector at index iwithin the plurality of ordered reference vectors, and wherein (C)comprises: (C)(1) identifying a peak match strength PK1 among the firstplurality of measures of similarity, wherein PK1 is the match strengthof MSV[j]; (C)(2) identifying a match strength PK_L, wherein PK_L is themaximum of match strengths MSV[j+RAD+1] and MSV[j−RAD−1], for apredetermined value of RAD; (C)(3) identifying a difference PK1-PK_L;and (C)(4) identifying the confidence estimate output based on PK1 andPK1-PK_L.
 16. The method of claim 6, wherein MSV[i] indicates a matchstrength at index i within the match strength vector, wherein R[i]indicates a reference vector at index i within the plurality of orderedreference vectors, wherein 0≦i<N, and wherein (C) comprises: (C)(1)identifying a peak match strength PK1 among the first plurality ofmeasures of similarity, wherein PK1 is the match strength of MSV[j];(C)(2) determining whether j>0; (C)(3) determining whether j<N−1; and(C)(4) identifying the confidence estimate output based on PK1 andwhether j>0 and whether j<N−1.
 17. The method of claim 6, wherein MSV[i]indicates a match strength at index i within the match strength vector,wherein R[i] indicates a reference vector at index i within theplurality of ordered reference vectors, wherein 0≦i<N, and wherein (C)comprises: (C)(1) identifying a peak match strength PK1 among the firstplurality of measures of similarity, wherein PK1 is the match strengthof MSV[j]; (C)(2) determining whether j>RAD, for a predetermined valueof RAD>0; (C)(3) determining whether j<N−RAD−1; and (C)(4) identifyingthe confidence estimate output based on PK1 and whether j>RAD andwhether j<N−RAD−1.
 18. The method of claim 1, wherein the first filtercomprises a plurality of filters.
 19. The method of claim 1, wherein thesecond filter comprises a plurality of filters.
 20. The method of claim1, wherein (C) comprises: (C)(1) selecting a first one of the firstfilter output and the second filter output as a first intermediatefilter output; (C)(2) selecting a second one of the first filter outputand the second filter output as a second intermediate filter output,wherein the first and second intermediate filter outputs differ fromeach other; (C)(3) deriving the confidence estimate output from thefirst intermediate filter output and the second intermediate filteroutput; (C)(4) applying an output transformation function to the firstintermediate filter output to produce a third filter output, wherein theconfidence estimate output represents a degree of confidence in thecorrectness of the third filter output; and (C)(5) providing the thirdfilter output and the confidence estimate output as output.
 21. Themethod of claim 20: wherein (C)(1) comprises selecting the firstintermediate filter output based on at least one input other than thedata vector and the set R; and wherein (C)(2) comprises selecting thesecond intermediate filter output based on the at least one input otherthan the data vector and the set R.
 22. A system comprising: a firstfilter comprising means for producing first filter output representing ameasure of similarity of a data vector to at least one reference vectorin a set R, wherein the set R contains at least one reference vector; asecond filter comprising means for producing second filter output basedon second filter inputs, wherein the first filter has at least onefailure mode not shared by the second filter; and confidence estimateoutput means for deriving, from the first filter output and the secondfilter output, confidence estimate output representing a degree ofconfidence in the correctness of the first filter output.
 23. The systemof claim 22, further comprising: output transformation means forapplying an output transformation function to the first filter output toproduce a third filter output, wherein the confidence estimate outputrepresents a degree of confidence in the correctness of the third filteroutput; and output provision means for providing the third filter outputand the confidence estimate output as output.
 24. The system of claim22, wherein the first filter inputs are the same as the second filterinputs.
 25. The system of claim 22, wherein the second filter inputs aredisjoint from the first filter inputs.
 26. The system of claim 22,wherein the second filter inputs include both the first filter inputsand additional filter inputs not contained within the first filterinputs.
 27. The system of claim 22, wherein the set R contains aplurality of reference vectors, and wherein the first filter outputcomprises a first plurality of measures of similarity specifying, foreach reference vector V in the set R, a strength of a match between thedata vector and the reference vector V, and wherein the confidenceestimate output means comprises means for selecting a first one of thefirst plurality of measures of similarity and means for deriving theconfidence estimate output from the first one of the first plurality ofmeasures of similarity and the second filter output.
 28. The system ofclaim 27, wherein the set R contains a plurality of reference vectors,and wherein the second filter output comprises a second plurality ofmeasures of similarity specifying, for each reference vector V in theset R, a strength of a match between the data vector and the referencevector V, and wherein the confidence estimate output means comprisesselecting a second one of the plurality of measures of similarity andmeans for deriving the confidence estimate output from the first one ofthe first plurality of measures of similarity and the second one of thesecond plurality of measures of similarity.
 29. The system of claim 27,wherein the confidence estimate output means comprises: means foridentifying a peak match strength PK1 among the first plurality ofmeasures of similarity; means for identifying a second-highest matchstrength PK2 among the first plurality of measures of similarity; meansfor identifying a quotient PK1/PK2; and means for identifying theconfidence estimate output based on PK1 and PK1/PK2.
 30. The system ofclaim 27, wherein the confidence estimate output means comprises: meansfor identifying a peak match strength PK1 among the first plurality ofmeasures of similarity; means for identifying a second-highest matchstrength PK2 among the first plurality of measures of similarity; meansfor identifying a difference PK1-PK2; and means for identifying theconfidence estimate output based on PK1 and PK1-PK2.
 31. The system ofclaim 27, wherein the confidence estimate output means comprises: meansfor identifying a peak match strength PK1 among the first plurality ofmeasures of similarity; means for identifying an average AVG of thefirst plurality of measures of similarity; means for identifying aquotient PK1/AVG; and means for identifying the confidence estimateoutput based on PK1 and PK1/AVG.
 32. The system of claim 27, wherein theconfidence estimate output means comprises: means for identifying a peakmatch strength PK1 among the first plurality of measures of similarity;means for identifying an average AVG of the first plurality of measuresof similarity; means for identifying a difference PK1-AVG; and means foridentifying the confidence estimate output based on PK1 and PK1-AVG. 33.The system of claim 27, wherein the confidence estimate output meanscomprises: means for identifying a peak match strength PK1 among thefirst plurality of measures of similarity; means for identifying anaverage AVG of the first plurality of measures of similarity; means foridentifying a standard deviation DEV of the first plurality of measuresof similarity; means for identifying a quotient PK1/(AVG+k*DEV), whereink is a constant; and means for identifying the confidence estimateoutput based on PK1 and (PK1/(AVG+k*DEV)).
 34. The system of claim 27,wherein the confidence estimate output means comprises: means foridentifying a peak match strength PK1 among the first plurality ofmeasures of similarity; means for identifying an average AVG of thefirst plurality of measures of similarity; means for identifying astandard deviation DEV of the first plurality of measures of similarity;means for identifying a difference PK1−(AVG+k*DEV), wherein k is aconstant; and means for identifying the confidence estimate output basedon PK1 and (PK1−(AVG+k*DEV)).
 35. The system of claim 27, wherein MSV[i]indicates a match strength at index i within the match strength vector,wherein R[i] indicates a reference vector at index i within theplurality of ordered reference vectors, and wherein the confidenceestimate output means comprises: means for identifying a peak matchstrength PK1 among the first plurality of measures of similarity,wherein PK1 is the match strength of MSV[j]; means for identifying amatch strength PK_L, wherein PK_L is the maximum of match strengthsMSV[j+RAD+1] and MSV[j−RAD−1], for a predetermined value of RAD; meansfor identifying a quotient PK1/PK_L; and means for identifying theconfidence estimate output based on PK1 and PK1/PK_L.
 36. The system ofclaim 27, wherein MSV[i] indicates a match strength at index i withinthe match strength vector, wherein R[i] indicates a reference vector atindex i within the plurality of ordered reference vectors, and whereinthe confidence estimate output means comprises: means for identifying apeak match strength PK1 among the first plurality of measures ofsimilarity, wherein PK1 is the match strength of MSV[j]; means foridentifying a match strength PK_L, wherein PK_L is the maximum of matchstrengths MSV[j+RAD+1] and MSV[j−RAD−1], for a predetermined value ofRAD; means for identifying a difference PK1-PK_L; and means foridentifying the confidence estimate output based on PK1 and PK1-PK_L.37. The system of claim 27, wherein MSV[i] indicates a match strength atindex i within the match strength vector, wherein R[i] indicates areference vector at index i within the plurality of ordered referencevectors, wherein 0≦i<N, and wherein the confidence estimate output meanscomprises: means for identifying a peak match strength PK1 among thefirst plurality of measures of similarity, wherein PK1 is the matchstrength of MSV[j]; means for determining whether j>0; means fordetermining whether j<N−1; and means for identifying the confidenceestimate output based on PK1 and whether j>0 and whether j<N−1.
 38. Thesystem of claim 27, wherein MSV[i] indicates a match strength at index iwithin the match strength vector, wherein R[i] indicates a referencevector at index i within the plurality of ordered reference vectors,wherein 0≦i<N, and wherein the confidence estimate output meanscomprises: means for identifying a peak match strength PK1 among thefirst plurality of measures of similarity, wherein PK1 is the matchstrength of MSV[j]; means for determining whether j>RAD, for apredetermined value of RAD>0; means for determining whether j<N−RAD−1;and means for identifying the confidence estimate output based on PK1and whether j>RAD and whether j<N−RAD−1.
 39. The system of claim 22,wherein the first filter comprises a plurality of filters.
 40. Thesystem of claim 22, wherein the second filter comprises a plurality offilters.
 41. The system of claim 22, wherein the confidence estimateoutput means comprises: first selection means for selecting a first oneof the first filter output and the second filter output as a firstintermediate filter output; second selection means for selecting asecond one of the first filter output and the second filter output as asecond intermediate filter output, wherein the first and secondintermediate filter outputs differ from each other; means for derivingthe confidence estimate output from the first intermediate filter outputand the second intermediate filter output; means for applying an outputtransformation function to the first intermediate filter output toproduce a third filter output, wherein the confidence estimate outputrepresents a degree of confidence in the correctness of the third filteroutput; and means for providing the third filter output and theconfidence estimate output as output.
 42. The system of claim 41:wherein the first means for selecting comprises means for selecting thefirst intermediate filter output based on at least one input other thanthe data vector and the set R; and wherein the second means forselecting comprises selecting the second intermediate filter outputbased on the at least one input other than the data vector and the setR.
 43. A method comprising: (A) receiving a first image input in animage input sequence; (B) receiving a second image input in the imageinput sequence, wherein the second image input has a relative positionsubsequent to the first image input in the image input sequence; (C)applying a frame sample filter to the first image input to produce asample pattern comprising at least one sample; (D) applying a sampleregistration filter to the second image input and the sample pattern toidentify: (1) an unfiltered flow sample set comprising at least oneoptical flow sample, wherein the unfiltered flow sample set defines acoregistration between the first image input and the second image input;(2) at least one fault warning representing a degree of confidence inthe coregistration; (E) removing, from the unfiltered flow sample set,at least one sample specified by the at least one fault warning asin-fault, to produce a filtered flow sample set; and (F) generatingoptical flow confidence estimate output based the filtered flow sampleset.
 44. The method of claim 43, wherein (F) comprises generating theoptical flow confidence estimate output based on the filtered flowsample set and at least one of: (1) a third input not contained withinthe first image input or the second image input; (2) the sample pattern;and (3) the at least one fault warning.
 45. The method of claim 43,wherein (F) comprises generating the optical flow confidence estimateoutput based on the filtered flow sample set and the third input. 46.The method of claim 43, wherein (C) comprises applying a matched filterto the first image input to produce the sample pattern.
 47. The methodof claim 43, wherein (D) comprises applying a matched filter to thesecond image input and the sample pattern to identify the unfilteredflow sample and the at least one fault warning.
 48. The method of claim43, wherein (C) comprises applying the frame sample filter to the firstimage input and the third input to produce the sample pattern.
 49. Themethod of claim 43, wherein (E) comprises determining which of thesamples in the unfiltered flow sample set to remove from the unfilteredflow sample set based on the third input and the at least one faultwarning.
 50. The method of claim 43, wherein the first matched filtercomprises a correntropy matched filter.
 51. The method of claim 43,wherein the second matched filter comprises a correntropy matchedfilter.
 52. A system comprising: means for receiving a first image inputin an image input sequence; means for receiving a second image input inthe image input sequence, wherein the second image input has a relativeposition subsequent to the first image input in the image inputsequence; a frame sample filter comprising means for producing, based onthe first image input, a sample pattern comprising at least one sample;a sample registration filter comprising means for identifying, based onthe second image input and the sample pattern: (1) an unfiltered flowsample set comprising at least one optical flow sample, wherein theunfiltered flow sample set defines a coregistration between the firstimage input and the second image input; (2) at least one fault warningrepresenting a degree of confidence in the coregistration; a flow samplefault filter comprising means for removing, from the unfiltered flowsample set, at least one sample specified by the at least one faultwarning as in-fault, to produce a filtered flow sample set; and anoptical flow confidence estimate generator to generate optical flowconfidence estimate output based the filtered flow sample set.
 53. Thesystem of claim 52, wherein the optical flow confidence estimategenerator comprises means for generating the optical flow confidenceestimate output based on the filtered flow sample set and at least oneof: (1) a third input not contained within the first image input or thesecond image input; (2) the sample pattern; and (3) the at least onefault warning.
 54. The system of claim 53, wherein the optical flowconfidence estimate generator comprises means for generating the opticalflow confidence estimate output based on the filtered flow sample setand the third input.
 55. The system of claim 52, wherein the framesample filter comprises a matched filter.
 56. The system of claim 52,wherein the sample registration filter comprises a matched filter. 57.The system of claim 52, wherein the frame sample filter comprises meansfor producing the sample pattern based on the first image input and thethird input.
 58. The system of claim 52, wherein the flow sample faultfilter comprises means for determining which of the samples in theunfiltered flow sample set to remove from the unfiltered flow sample setbased on the third input and the at least one fault warning.
 59. Thesystem of claim 52, wherein the first matched filter comprises acorrentropy matched filter.
 60. The system of claim 52, wherein thesecond matched filter comprises a correntropy matched filter.
 61. Amethod comprising: (A) receiving a first image input in an image inputsequence; (B) receiving a second image input in the image inputsequence, wherein the second image input has a relative positionsubsequent to the first image input in the image input sequence; (C)applying a frame sample filter, including a first matched filter, to thefirst image input to produce a sample pattern comprising at least onesample; (D) applying a sample registration filter, including a secondmatched filter, to the second image input and the sample pattern toidentify: (1) an unfiltered flow sample set comprising at least oneoptical flow sample, wherein the unfiltered flow sample set defines acoregistration between the first image input and the second image input;and (2) at least one fault warning representing a degree of confidencein the coregistration; and (E) removing, from the unfiltered flow sampleset, at least one sample specified by the at least one fault warning asin-fault, to produce a filtered flow sample set.
 62. The method of claim61, further comprising: (F) generating optical flow confidence estimateoutput based the filtered flow sample set and at least one of: (1) athird input not contained within the first image input or the secondimage input; (2) the sample pattern; and (3) the at least one faultwarning.
 63. The method of claim 61, wherein (C) comprises applying theframe sample filter to the first image input and the third input toproduce the sample pattern.
 64. The method of claim 61, wherein (E)comprises determining which of the samples in the unfiltered flow sampleset to remove from the unfiltered flow sample set based on the thirdinput and the at least one fault warning.
 65. The method of claim 61,wherein the first matched filter comprises a correntropy matched filter.66. The method of claim 61, wherein the second matched filter comprisesa correntropy matched filter.
 67. A system comprising: means forreceiving a first image input in an image input sequence; means forreceiving a second image input in the image input sequence, wherein thesecond image input has a relative position subsequent to the first imageinput in the image input sequence; a frame sample filter comprising afirst matched filter, the matched filter comprising means for producing,based on the first image input, a sample pattern comprising at least onesample; a sample registration filter comprising means for identifying,based on the second image input and the sample pattern: (1) anunfiltered flow sample set comprising at least one optical flow sample,wherein the unfiltered flow sample set defines a coregistration betweenthe first image input and the second image input; and (2) at least onefault warning representing a degree of confidence in the coregistration;and a flow sample fault filter comprising means for removing, from theunfiltered flow sample set, at least one sample specified by the atleast one fault warning as in-fault, to produce a filtered flow sampleset.
 68. The method of claim 67, further comprising: an optical flowconfidence estimate generator to generate optical flow confidenceestimate output based the filtered flow sample set and at least one of:(1) a third input not contained within the first image input or thesecond image input; (2) the sample pattern; and (3) the at least onefault warning.
 69. The system of claim 67, wherein the frame samplefilter comprises means for producing the sample pattern based on thefirst image input and the third input.
 70. The system of claim 67,wherein the flow sample filter comprises means for determining which ofthe samples in the unfiltered flow sample set to remove from theunfiltered flow sample set based on the third input and the at least onefault warning.
 71. The system of claim 67, wherein the first matchedfilter comprises a correntropy matched filter.
 72. The system of claim67, wherein the second matched filter comprises a correntropy matchedfilter.